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We just submitted our project to the OpenAI Developers WebMCP hackathon: The WebMCP Computer. WebMCP Computer is an operating system that lives inside the browser, with WebMCP as its native control layer. It is a computer that treats agents as first-class citizens, where files, processes, applications, windows, and the...

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idan levin

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23,948 views • 2 days ago •via X (Twitter)

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WebMCP by Microsoft and Google is HERE and I'm surprised more people aren't talking about it. Why does it matter? BILLIONS of dollars are about to move through agents, and the internet isn't built for them yet. 1. SEO was about Google understanding your page. 2. AEO was about AI citing you. 3. WebMCP is about the agent actually finishing the job (buying, researching, requesting quote etc). WebMCP is basically websites with agent buttons. Instead of an agent scanning a page like a human, screenshotting and guessing where to click, the site just tells it "here's how to search, here's how to book, here's how to buy." 2 cash flowing businesses you could start today using WebMCP: 1. A WebMCP conversion agency Make boring business websites agent-ready. Law firms, HVAC, med spas, dentists. Build them the first tools (request a quote, book a consult), sell the setup for $2k, then charge a few hundred a month to monitor and improve it. 2. An agent mystery shopper. Test whether agents can actually complete the important journeys on someone's site, buying the hoodie, booking the consult, filing the claim. Hand them a report on where the agent got stuck, missing tools, bad descriptions, lost conversions. Charge monthly, then turn the repeated fixes into software. Full episode on The Startup Ideas Podcast (SIP) 🧃 with Vinny below You'll learn what WebMCP really is, see examples of it IN ACTION, and hear more about 2 startup ideas using WebMCP you can start today. WebMCP is pretty cool. Watch.
28:57

WebMCP by Microsoft and Google is HERE and I'm surprised more people aren't talking about it. Why does it matter? BILLIONS of dollars are about to move through agents, and the internet isn't built for them yet. 1. SEO was about Google understanding your page. 2. AEO was about AI citing you. 3. WebMCP is about the agent actually finishing the job (buying, researching, requesting quote etc). WebMCP is basically websites with agent buttons. Instead of an agent scanning a page like a human, screenshotting and guessing where to click, the site just tells it "here's how to search, here's how to book, here's how to buy." 2 cash flowing businesses you could start today using WebMCP: 1. A WebMCP conversion agency Make boring business websites agent-ready. Law firms, HVAC, med spas, dentists. Build them the first tools (request a quote, book a consult), sell the setup for $2k, then charge a few hundred a month to monitor and improve it. 2. An agent mystery shopper. Test whether agents can actually complete the important journeys on someone's site, buying the hoodie, booking the consult, filing the claim. Hand them a report on where the agent got stuck, missing tools, bad descriptions, lost conversions. Charge monthly, then turn the repeated fixes into software. Full episode on The Startup Ideas Podcast (SIP) 🧃 with Vinny below You'll learn what WebMCP really is, see examples of it IN ACTION, and hear more about 2 startup ideas using WebMCP you can start today. WebMCP is pretty cool. Watch.

GREG ISENBERG

178,527 views • 10 days ago

WebMCP is the future of the web. Agents can now interact with any website without ever seeing the UI. I built a starter template to show how: A DoorDash like app where the agent adds items to cart and checks out with the right address + promo code. The browser is now the API.
0:31

WebMCP is the future of the web. Agents can now interact with any website without ever seeing the UI. I built a starter template to show how: A DoorDash like app where the agent adds items to cart and checks out with the right address + promo code. The browser is now the API.

Pietro Schirano

168,427 views • 6 months ago

We just open-sourced Simulang, a JavaScript library that gives you and your coding agents the ability to fully control computers. Simulang is our scripting framework for desktop automation. It allows you to write deterministic computer use code that can be replayed dozens if not hundreds of times. Much faster and cheaper, compared to LLM agents. It ships with a skill out of the box. That means you can turn natural language prompts into Simulang code directly, and your agent can now see the screen, click, type, and navigate. It gives your AI agents eyes and hands to control the computer like humans do. Try these things with Simulang: - upload your pile of invoices to the expense platform - find 50 people with specific criteria on LinkedIn - open your calculator app and do some math - find all buttons on your favorite app using accessibility tree - or draw a bunny with MS paint 🐰 In this demo, we show how Simulang took over when the coding agent couldn’t open and click on the target LinkedIn profile page. Give it a try:
1:08

We just open-sourced Simulang, a JavaScript library that gives you and your coding agents the ability to fully control computers. Simulang is our scripting framework for desktop automation. It allows you to write deterministic computer use code that can be replayed dozens if not hundreds of times. Much faster and cheaper, compared to LLM agents. It ships with a skill out of the box. That means you can turn natural language prompts into Simulang code directly, and your agent can now see the screen, click, type, and navigate. It gives your AI agents eyes and hands to control the computer like humans do. Try these things with Simulang: - upload your pile of invoices to the expense platform - find 50 people with specific criteria on LinkedIn - open your calculator app and do some math - find all buttons on your favorite app using accessibility tree - or draw a bunny with MS paint 🐰 In this demo, we show how Simulang took over when the coding agent couldn’t open and click on the target LinkedIn profile page. Give it a try:

Simular

14,468 views • 3 months ago

People are still sleeping on WebMCP.. You can now give agents tool calls to interact with your website natively. Including full UI/UX support and interactive elements to build their own views. Sharing a demo below that I submitted for the WebMCP challenge - see how agents can interact with the El Nino situation tracker. It creates it's own views, preserves human edits, and creates a shareable link to share with other agents (or humans). Video and code by Codex ChatGPT & Railway Live at elneenyo dot com to try out today!
0:49

People are still sleeping on WebMCP.. You can now give agents tool calls to interact with your website natively. Including full UI/UX support and interactive elements to build their own views. Sharing a demo below that I submitted for the WebMCP challenge - see how agents can interact with the El Nino situation tracker. It creates it's own views, preserves human edits, and creates a shareable link to share with other agents (or humans). Video and code by Codex ChatGPT & Railway Live at elneenyo dot com to try out today!

Nikunj Kothari

20,300 views • 3 days ago

OpenAI just recreated Samantha from the movie "Her" This means we can finally have agents that do computer work as we talk to them in real time. Imagine the world when your agent creates thousands of versions of itself, each with its own Orgo computer... That's the future, and we're living it today.
2:27

OpenAI just recreated Samantha from the movie "Her" This means we can finally have agents that do computer work as we talk to them in real time. Imagine the world when your agent creates thousands of versions of itself, each with its own Orgo computer... That's the future, and we're living it today.

Nick Vasilescu

197,861 views • 3 months ago

Perplexity just launched "Personal Computer", an always-on AI agent that merges their cloud-based Computer system with a Mac Mini. It accesses local files, apps, and sessions, orchestrates 19+ AI models, and can run 24/7. The Mac Mini continues to have its AI moment:
1:27

Perplexity just launched "Personal Computer", an always-on AI agent that merges their cloud-based Computer system with a Mac Mini. It accesses local files, apps, and sessions, orchestrates 19+ AI models, and can run 24/7. The Mac Mini continues to have its AI moment:

The Rundown AI

20,854 views • 5 months ago

Agent-in-the-browser unlocks superpowers. The magic really shines when you bring your own model, system prompts, and MCP tools into the mix. Hence, AgentBoard 🪄. Your models, keys, and settings. Integration with remote MCP servers and — this is where it gets really fun — ability to register and allow the agent to call custom ~WebMCP tools that execute in context of the page. Chrome Web Store: Code:
3:51

Agent-in-the-browser unlocks superpowers. The magic really shines when you bring your own model, system prompts, and MCP tools into the mix. Hence, AgentBoard 🪄. Your models, keys, and settings. Integration with remote MCP servers and — this is where it gets really fun — ability to register and allow the agent to call custom ~WebMCP tools that execute in context of the page. Chrome Web Store: Code:

Ilya Grigorik

22,961 views • 10 months ago

Our first short course with Anthropic! Building Towards Computer Use with Anthropic. This teaches you to build an LLM-based agent that uses a computer interface by generating mouse clicks and keystrokes. Computer Use is an important, emerging capability for LLMs that will let AI agents do many more tasks than were possible before, since it lets them interact with interfaces designed for humans to use, rather than only tools that provide explicit API access. I hope you will enjoy learning about it! This course is taught by Anthropic's Head of Curriculum, Colt_Steele. You'll learn to apply image reasoning and tool use to "use" a computer as follows: a model processes an image of the screen, analyzes it to understand what's going on, and navigates the computer via mouse clicks and keystrokes. This course goes through the key building blocks, and culminates in a demo of an AI assistant that uses a web browser to search for a research paper, downloads the PDF, and finally summarizes the paper for you. In detail, you’ll: - Learn about Anthropic's family of models, when to use which one, and make API requests to Claude - Use multi-modal prompts that combine text and image content blocks, and also work with streaming responses - Improve your prompting by using prompt templates, using XML to structure prompts, and providing examples - Implement prompt caching to reduce cost and latency - Apply tool-use to build a chatbot that can call different tools to respond to queries - See all these building blocks come together in Computer Use demo Please sign up here:
1:56

Our first short course with Anthropic! Building Towards Computer Use with Anthropic. This teaches you to build an LLM-based agent that uses a computer interface by generating mouse clicks and keystrokes. Computer Use is an important, emerging capability for LLMs that will let AI agents do many more tasks than were possible before, since it lets them interact with interfaces designed for humans to use, rather than only tools that provide explicit API access. I hope you will enjoy learning about it! This course is taught by Anthropic's Head of Curriculum, Colt_Steele. You'll learn to apply image reasoning and tool use to "use" a computer as follows: a model processes an image of the screen, analyzes it to understand what's going on, and navigates the computer via mouse clicks and keystrokes. This course goes through the key building blocks, and culminates in a demo of an AI assistant that uses a web browser to search for a research paper, downloads the PDF, and finally summarizes the paper for you. In detail, you’ll: - Learn about Anthropic's family of models, when to use which one, and make API requests to Claude - Use multi-modal prompts that combine text and image content blocks, and also work with streaming responses - Improve your prompting by using prompt templates, using XML to structure prompts, and providing examples - Implement prompt caching to reduce cost and latency - Apply tool-use to build a chatbot that can call different tools to respond to queries - See all these building blocks come together in Computer Use demo Please sign up here:

Andrew Ng

170,425 views • 1 year ago

This is a Commodore 1541 5.25" floppy disk drive. It functions as a computer in its own right, with its own 8-bit CPU, ROM, and operating system (CBM DOS). It is its own computer with its own operating system. The Commodore 64 or 128 or Vic 20 or whatever would talk to it over a serial cable and send commands to the onboard computer. These drives were so robust and overbuilt.
3:00

This is a Commodore 1541 5.25" floppy disk drive. It functions as a computer in its own right, with its own 8-bit CPU, ROM, and operating system (CBM DOS). It is its own computer with its own operating system. The Commodore 64 or 128 or Vic 20 or whatever would talk to it over a serial cable and send commands to the onboard computer. These drives were so robust and overbuilt.

RossRadio

53,661 views • 6 months ago

Today we are launching Openwork is now Accomplish, an open-source (MIT-licensed) computer-use agent that’s fast, cheap, and more secure. Openwork is now Accomplish is the result of a short two-day hackathon our team decided to hack, which brings together some of our favorite open source AI modules into one powerful agent, to allow you to: 1. Bring your own model/API key (any provider and model supported by OpenCode is supported by Openwork) 2. ~4x faster than Claude for Chrome/Cowork, and much more token-efficient, powered by dev-browser by Sawyer Hood (legend) 3. More secure - contrary to Claude for Chrom/Cowork, does not leverage the main browser instance where you are logged into all services already. You login only to the services you need. This significantly reduces the risk of data loss in case of prompt injections, to which computer-use agents are highly exposed. 4. Free and 100% open-source! You can download the DMG (macOS only for now) or fork the github repo via the link in bio (Openwork is now Accomplish). Let us know what you think (or better, send a pull request)!
1:23

Today we are launching Openwork is now Accomplish, an open-source (MIT-licensed) computer-use agent that’s fast, cheap, and more secure. Openwork is now Accomplish is the result of a short two-day hackathon our team decided to hack, which brings together some of our favorite open source AI modules into one powerful agent, to allow you to: 1. Bring your own model/API key (any provider and model supported by OpenCode is supported by Openwork) 2. ~4x faster than Claude for Chrome/Cowork, and much more token-efficient, powered by dev-browser by Sawyer Hood (legend) 3. More secure - contrary to Claude for Chrom/Cowork, does not leverage the main browser instance where you are logged into all services already. You login only to the services you need. This significantly reduces the risk of data loss in case of prompt injections, to which computer-use agents are highly exposed. 4. Free and 100% open-source! You can download the DMG (macOS only for now) or fork the github repo via the link in bio (Openwork is now Accomplish). Let us know what you think (or better, send a pull request)!

Or Hiltch

1,003,492 views • 7 months ago

WebMCP makes sites agent-native. The industries that need it first: - any complicated web store - SaaS admin boards and consoles - analytics and marketing tools - read-only helpers in insurance or banking self-service That last one is the unlock for regulated industries. A few tools that read browser state, touch nothing, and either point the user to the right screen or pull the right info for their agent. But as a founder, you don't need any of that for your customers right away. Build it in-house first. Your internal tools, your own team, your own workflow. That's where the potential is.
2:11

WebMCP makes sites agent-native. The industries that need it first: - any complicated web store - SaaS admin boards and consoles - analytics and marketing tools - read-only helpers in insurance or banking self-service That last one is the unlock for regulated industries. A few tools that read browser state, touch nothing, and either point the user to the right screen or pull the right info for their agent. But as a founder, you don't need any of that for your customers right away. Build it in-house first. Your internal tools, your own team, your own workflow. That's where the potential is.

The Startup Ideas Podcast (SIP) 🧃

10,546 views • 9 days ago

What if you can build an Agent with it own computer in a single api call? At my Google I/O talk, I showed how to use Gemini Managed Agents and the new Interactions API to give your AI a secure, hosted Linux sandbox to execute code and manage its own memory.
23:49

What if you can build an Agent with it own computer in a single api call? At my Google I/O talk, I showed how to use Gemini Managed Agents and the new Interactions API to give your AI a secure, hosted Linux sandbox to execute code and manage its own memory.

Philipp Schmid

21,769 views • 3 months ago

This agent relies on a marketplace of 2.8M+ specialized agents to complete a task. This is how I imagine the future of personal agents. Here is how this works: 1. Agentverse is a marketplace of agents 2. Anyone can build and publish an agent there 3. ASI:One is a personal agent with access to the marketplace 4. To solve a task, ASI:One finds the best agent for the job In other words: You have an agent that can solve pretty much *anything* because it knows how to outsource the work to a network of specialized agents. And you don't need to do any setup to get this working. The way ranking agents in the marketplace works is pretty cool: Fetch.ai, the company behind it, created an AgentRank algorithm that works much like Google's PageRank. Every time an agent calls another, it forms a connection. Over time, these connections build into a graph of who relies on whom. An agent that is frequently called by other reputable agents scores higher and surfaces higher in results. The end result here is that your agent will consistently use the best agents in the marketplace to accomplish a goal. You should give this a try: You don't need to pay or even register to try these agents. Thanks to the team for partnering with me on this post.
4:16

This agent relies on a marketplace of 2.8M+ specialized agents to complete a task. This is how I imagine the future of personal agents. Here is how this works: 1. Agentverse is a marketplace of agents 2. Anyone can build and publish an agent there 3. ASI:One is a personal agent with access to the marketplace 4. To solve a task, ASI:One finds the best agent for the job In other words: You have an agent that can solve pretty much *anything* because it knows how to outsource the work to a network of specialized agents. And you don't need to do any setup to get this working. The way ranking agents in the marketplace works is pretty cool: Fetch.ai, the company behind it, created an AgentRank algorithm that works much like Google's PageRank. Every time an agent calls another, it forms a connection. Over time, these connections build into a graph of who relies on whom. An agent that is frequently called by other reputable agents scores higher and surfaces higher in results. The end result here is that your agent will consistently use the best agents in the marketplace to accomplish a goal. You should give this a try: You don't need to pay or even register to try these agents. Thanks to the team for partnering with me on this post.

Santiago

18,715 views • 2 months ago

We keep defaulting to the biggest model in the room every time we touch AI in our apps. Claude, GPT, Gemini. Cloud or nothing. But the browser is already more capable than most people realize: WebAI and WebMCP may change this. With WebAI you can run models client-side. With WebMCP, your site can expose real actions as JavaScript functions. Agents don't need screenshots, DOM scraping, or blind clicking. They can just use your app. I talked about this with Guy Podjarny on Tessl AI Native Dev podcast: the browser is turning into an AI runtime. Links in first reply :)
1:12

We keep defaulting to the biggest model in the room every time we touch AI in our apps. Claude, GPT, Gemini. Cloud or nothing. But the browser is already more capable than most people realize: WebAI and WebMCP may change this. With WebAI you can run models client-side. With WebMCP, your site can expose real actions as JavaScript functions. Agents don't need screenshots, DOM scraping, or blind clicking. They can just use your app. I talked about this with Guy Podjarny on Tessl AI Native Dev podcast: the browser is turning into an AI runtime. Links in first reply :)

Maximiliano Firtman

14,824 views • 5 months ago

happy friday. sneak peek at some new features i'm building for the bankr agent: > sandboxed filesystem (your agent gets its own secure file system in the browser. you control what it can access. no agent running loose on your actual computer) > skill uploads (plugin new capabilities so your agent learns new things) > cli download & access > secure environment variables for api keys > github integration (connect your repo for reads and writes directly from your agent) all of this runs in a secure browser environment. no desktop app. no messy configuration. no downloading agent harnesses from the terminal. just a sandbox you control. in the video: i download an audit skill, run it against a smart contract, and save the report straight to the filesystem. then i ask the agent to pull all my 2026 transactions and write a csv for my accountant.
0:58

happy friday. sneak peek at some new features i'm building for the bankr agent: > sandboxed filesystem (your agent gets its own secure file system in the browser. you control what it can access. no agent running loose on your actual computer) > skill uploads (plugin new capabilities so your agent learns new things) > cli download & access > secure environment variables for api keys > github integration (connect your repo for reads and writes directly from your agent) all of this runs in a secure browser environment. no desktop app. no messy configuration. no downloading agent harnesses from the terminal. just a sandbox you control. in the video: i download an audit skill, run it against a smart contract, and save the report straight to the filesystem. then i ask the agent to pull all my 2026 transactions and write a csv for my accountant.

deployer

45,638 views • 4 months ago

🚀 The team at Google just released the Agents API, a service for building and running custom agents inside a sandboxed Linux environment, and we built a template that gives these agents access to LlamaParse / LiteParse, enabling them to process unstructured documents automatically 📄⚡ Here’s how it works: 🔹 Configure a Git repository where data and outputs will be stored 🔹 Clone the repository into the agent sandbox 🔹 Install the LiteParse CLI, the LlamaParse SDK, and agent skills to use both 🔹 Prompt the agent with a task and watch it process documents autonomously 🤖 The result? An agent that can work directly with messy, real-world documents using LlamaParse and LiteParse within Google’s new agent runtime. Check out the GitHub repository: Get started with LlamaParse:
0:32

🚀 The team at Google just released the Agents API, a service for building and running custom agents inside a sandboxed Linux environment, and we built a template that gives these agents access to LlamaParse / LiteParse, enabling them to process unstructured documents automatically 📄⚡ Here’s how it works: 🔹 Configure a Git repository where data and outputs will be stored 🔹 Clone the repository into the agent sandbox 🔹 Install the LiteParse CLI, the LlamaParse SDK, and agent skills to use both 🔹 Prompt the agent with a task and watch it process documents autonomously 🤖 The result? An agent that can work directly with messy, real-world documents using LlamaParse and LiteParse within Google’s new agent runtime. Check out the GitHub repository: Get started with LlamaParse:

LlamaIndex 🦙

21,975 views • 3 months ago

working with cloud agents in cursor feels the same as local, but the fact that i can shut down my computer and still have it running is amazing. one of my favorite use cases is planning in cursor, then send off to cloud agent for impl. here's a short demo
0:32

working with cloud agents in cursor feels the same as local, but the fact that i can shut down my computer and still have it running is amazing. one of my favorite use cases is planning in cursor, then send off to cloud agent for impl. here's a short demo

eric zakariasson

61,966 views • 10 months ago

you don't have to keep your laptop open for your agents to keep running just type /handoff and send your agent to the cloud with Devin (and close your laptop) from there, your agent gets: - its own Linux VM - shell, IDE, browser - full desktop Computer Use - end-to-end test recordings - ready-to-review PRs - it's own review agent you can continue your session from your phone, computer, or anywhere with an internet connection and you can send as many sessions as you'd like in parallel.
0:35

you don't have to keep your laptop open for your agents to keep running just type /handoff and send your agent to the cloud with Devin (and close your laptop) from there, your agent gets: - its own Linux VM - shell, IDE, browser - full desktop Computer Use - end-to-end test recordings - ready-to-review PRs - it's own review agent you can continue your session from your phone, computer, or anywhere with an internet connection and you can send as many sessions as you'd like in parallel.

nader dabit

61,367 views • 4 months ago

Imagine a computer where you don’t need to learn 10 apps to get work done. You just tell it what you want, and it adapts to how you work. I tested Happycapy with a real use case and created an image and a video. No coding, no complex software. Do this: - Build automations that run on schedule - Deploy agent teams that work for you AI molds how you work, not the other way around. If you can use a computer, you can make anything happen. Start your first Automation and Agent teams today.
0:24

Imagine a computer where you don’t need to learn 10 apps to get work done. You just tell it what you want, and it adapts to how you work. I tested Happycapy with a real use case and created an image and a video. No coding, no complex software. Do this: - Build automations that run on schedule - Deploy agent teams that work for you AI molds how you work, not the other way around. If you can use a computer, you can make anything happen. Start your first Automation and Agent teams today.

Aaliya

17,042 views • 6 months ago